






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin






class 			_UpperCamelCase(       __lowerCamelCase		,				__lowerCamelCase		):

		@register_to_config
		def __init__(					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    int = 1_2_8    ,       SCREAMING_SNAKE_CASE__		:    int = 2_5_6    ,       SCREAMING_SNAKE_CASE__		:    float = 2_000.0    ,       SCREAMING_SNAKE_CASE__		:    int = 7_6_8    ,       SCREAMING_SNAKE_CASE__		:    int = 1_2    ,       SCREAMING_SNAKE_CASE__		:    int = 1_2    ,       SCREAMING_SNAKE_CASE__		:    int = 6_4    ,       SCREAMING_SNAKE_CASE__		:    int = 2_0_4_8    ,       SCREAMING_SNAKE_CASE__		:    float = 0.1    ,       ):



					'''simple docstring'''



					super().__init__()

					__a						:  List[str]									=     nn.Sequential(
					    nn.Linear(SCREAMING_SNAKE_CASE__    ,       d_model * 4    ,       bias=SCREAMING_SNAKE_CASE__    )    ,       nn.SiLU()    ,       nn.Linear(d_model * 4    ,       d_model * 4    ,       bias=SCREAMING_SNAKE_CASE__    )    ,       nn.SiLU()    ,       )

					__a						:  Optional[Any]									=     nn.Embedding(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[Any]									=     False

					__a						:  Tuple									=     nn.Linear(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       bias=SCREAMING_SNAKE_CASE__    )

					__a						:  Tuple									=     nn.Dropout(p=SCREAMING_SNAKE_CASE__    )

					__a						:  Union[str, Any]									=     nn.ModuleList()
					for lyr_num in range(SCREAMING_SNAKE_CASE__    ):
								# FiLM conditional T5 decoder
								__a						:  List[Any]									=     DecoderLayer(d_model=SCREAMING_SNAKE_CASE__    ,       d_kv=SCREAMING_SNAKE_CASE__    ,       num_heads=SCREAMING_SNAKE_CASE__    ,       d_ff=SCREAMING_SNAKE_CASE__    ,       dropout_rate=SCREAMING_SNAKE_CASE__    )
								self.decoders.append(SCREAMING_SNAKE_CASE__    )

					__a						:  Union[str, Any]									=     TaLayerNorm(SCREAMING_SNAKE_CASE__    )

					__a						:  Any									=     nn.Dropout(p=SCREAMING_SNAKE_CASE__    )
					__a						:  Union[str, Any]									=     nn.Linear(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       bias=SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Any    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  Any									=     torch.mul(query_input.unsqueeze(-1    )    ,       key_input.unsqueeze(-2    )    )
					return mask.unsqueeze(-3    )



		def    __lowerCAmelCase  (					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					__a						,					__a						,					__a						:  Optional[int]									=     decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					__a						:  Optional[int]									=     get_timestep_embedding(
					    decoder_noise_time * self.config.max_decoder_noise_time    ,       embedding_dim=self.config.d_model    ,       max_period=self.config.max_decoder_noise_time    ,       ).to(dtype=self.dtype    )

					__a						:  List[Any]									=     self.conditioning_emb(SCREAMING_SNAKE_CASE__    ).unsqueeze(1    )

					assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

					__a						:  Optional[Any]									=     decoder_input_tokens.shape[1]

					# If we want to use relative positions for audio context, we can just offset
					# this sequence by the length of encodings_and_masks.
					__a						:  int									=     torch.broadcast_to(
					    torch.arange(SCREAMING_SNAKE_CASE__    ,       device=decoder_input_tokens.device    )    ,       (batch, seq_length)    ,       )

					__a						:  Any									=     self.position_encoding(SCREAMING_SNAKE_CASE__    )

					__a						:  Any									=     self.continuous_inputs_projection(SCREAMING_SNAKE_CASE__    )
					inputs += position_encodings
					__a						:  Union[str, Any]									=     self.dropout(SCREAMING_SNAKE_CASE__    )

					# decoder: No padding present.
					__a						:  Optional[int]									=     torch.ones(
					    decoder_input_tokens.shape[:2]    ,       device=decoder_input_tokens.device    ,       dtype=inputs.dtype    )

					# Translate encoding masks to encoder-decoder masks.
					__a						:  List[Any]									=     [(x, self.encoder_decoder_mask(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )) for x, y in encodings_and_masks]

					# cross attend style: concat encodings
					__a						:  Optional[Any]									=     torch.cat([x[0] for x in encodings_and_encdec_masks]    ,       dim=1    )
					__a						:  List[str]									=     torch.cat([x[1] for x in encodings_and_encdec_masks]    ,       dim=-1    )

					for lyr in self.decoders:
								__a						:  Optional[Any]									=     lyr(
								    SCREAMING_SNAKE_CASE__    ,       conditioning_emb=SCREAMING_SNAKE_CASE__    ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__    ,       encoder_attention_mask=SCREAMING_SNAKE_CASE__    ,       )[0]

					__a						:  Optional[int]									=     self.decoder_norm(SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[int]									=     self.post_dropout(SCREAMING_SNAKE_CASE__    )

					__a						:  str									=     self.spec_out(SCREAMING_SNAKE_CASE__    )
					return spec_out






class 			_UpperCamelCase(       nn.Module		):

		def __init__(					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    int    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=1e-6    ):



					'''simple docstring'''



					super().__init__()
					__a						:  str									=     nn.ModuleList()

					# cond self attention: layer 0
					self.layer.append(
					    TaLayerSelfAttentionCond(d_model=SCREAMING_SNAKE_CASE__    ,       d_kv=SCREAMING_SNAKE_CASE__    ,       num_heads=SCREAMING_SNAKE_CASE__    ,       dropout_rate=SCREAMING_SNAKE_CASE__    )    )

					# cross attention: layer 1
					self.layer.append(
					    TaLayerCrossAttention(
					        d_model=SCREAMING_SNAKE_CASE__    ,       d_kv=SCREAMING_SNAKE_CASE__    ,       num_heads=SCREAMING_SNAKE_CASE__    ,       dropout_rate=SCREAMING_SNAKE_CASE__    ,       layer_norm_epsilon=SCREAMING_SNAKE_CASE__    ,       )    )

					# Film Cond MLP + dropout: last layer
					self.layer.append(
					    TaLayerFFCond(d_model=SCREAMING_SNAKE_CASE__    ,       d_ff=SCREAMING_SNAKE_CASE__    ,       dropout_rate=SCREAMING_SNAKE_CASE__    ,       layer_norm_epsilon=SCREAMING_SNAKE_CASE__    )    )



		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=None    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=None    ,       SCREAMING_SNAKE_CASE__		:    Any=None    ,       SCREAMING_SNAKE_CASE__		:    int=None    ,       ):



					'''simple docstring'''



					__a						:  List[str]									=     self.layer[0](
					    SCREAMING_SNAKE_CASE__    ,       conditioning_emb=SCREAMING_SNAKE_CASE__    ,       attention_mask=SCREAMING_SNAKE_CASE__    ,       )

					if encoder_hidden_states is not None:
								__a						:  List[str]									=     torch.where(encoder_attention_mask > 0    ,       0    ,       -1e10    ).to(
								    encoder_hidden_states.dtype    )

								__a						:  str									=     self.layer[1](
								    SCREAMING_SNAKE_CASE__    ,       key_value_states=SCREAMING_SNAKE_CASE__    ,       attention_mask=SCREAMING_SNAKE_CASE__    ,       )

					# Apply Film Conditional Feed Forward layer
					__a						:  List[str]									=     self.layer[-1](SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

					return (hidden_states,)






class 			_UpperCamelCase(       nn.Module		):

		def __init__(					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ):



					'''simple docstring'''



					super().__init__()
					__a						:  int									=     TaLayerNorm(SCREAMING_SNAKE_CASE__    )
					__a						:  str									=     TaFiLMLayer(in_features=d_model * 4    ,       out_features=SCREAMING_SNAKE_CASE__    )
					__a						:  int									=     Attention(query_dim=SCREAMING_SNAKE_CASE__    ,       heads=SCREAMING_SNAKE_CASE__    ,       dim_head=SCREAMING_SNAKE_CASE__    ,       out_bias=SCREAMING_SNAKE_CASE__    ,       scale_qk=SCREAMING_SNAKE_CASE__    )
					__a						:  int									=     nn.Dropout(SCREAMING_SNAKE_CASE__    )



		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=None    ,       SCREAMING_SNAKE_CASE__		:    int=None    ,       ):



					'''simple docstring'''



					__a						:  List[Any]									=     self.layer_norm(SCREAMING_SNAKE_CASE__    )

					if conditioning_emb is not None:
								__a						:  int									=     self.FiLMLayer(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

					# Self-attention block
					__a						:  List[str]									=     self.attention(SCREAMING_SNAKE_CASE__    )

					__a						:  Union[str, Any]									=     hidden_states + self.dropout(SCREAMING_SNAKE_CASE__    )

					return hidden_states






class 			_UpperCamelCase(       nn.Module		):

		def __init__(					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Any    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ):



					'''simple docstring'''



					super().__init__()
					__a						:  int									=     Attention(query_dim=SCREAMING_SNAKE_CASE__    ,       heads=SCREAMING_SNAKE_CASE__    ,       dim_head=SCREAMING_SNAKE_CASE__    ,       out_bias=SCREAMING_SNAKE_CASE__    ,       scale_qk=SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[int]									=     TaLayerNorm(SCREAMING_SNAKE_CASE__    ,       eps=SCREAMING_SNAKE_CASE__    )
					__a						:  Dict									=     nn.Dropout(SCREAMING_SNAKE_CASE__    )



		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Any    ,       SCREAMING_SNAKE_CASE__		:    Tuple=None    ,       SCREAMING_SNAKE_CASE__		:    Tuple=None    ,       ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     self.layer_norm(SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[Any]									=     self.attention(
					    SCREAMING_SNAKE_CASE__    ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__    ,       attention_mask=attention_mask.squeeze(1    )    ,       )
					__a						:  int									=     hidden_states + self.dropout(SCREAMING_SNAKE_CASE__    )
					return layer_output






class 			_UpperCamelCase(       nn.Module		):

		def __init__(					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					super().__init__()
					__a						:  str									=     TaDenseGatedActDense(d_model=SCREAMING_SNAKE_CASE__    ,       d_ff=SCREAMING_SNAKE_CASE__    ,       dropout_rate=SCREAMING_SNAKE_CASE__    )
					__a						:  Tuple									=     TaFiLMLayer(in_features=d_model * 4    ,       out_features=SCREAMING_SNAKE_CASE__    )
					__a						:  Union[str, Any]									=     TaLayerNorm(SCREAMING_SNAKE_CASE__    ,       eps=SCREAMING_SNAKE_CASE__    )
					__a						:  Dict									=     nn.Dropout(SCREAMING_SNAKE_CASE__    )



		def    __lowerCAmelCase  (					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[str]=None    ):



					'''simple docstring'''



					__a						:  int									=     self.layer_norm(SCREAMING_SNAKE_CASE__    )
					if conditioning_emb is not None:
								__a						:  Optional[int]									=     self.film(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

					__a						:  str									=     self.DenseReluDense(SCREAMING_SNAKE_CASE__    )
					__a						:  Dict									=     hidden_states + self.dropout(SCREAMING_SNAKE_CASE__    )
					return hidden_states






class 			_UpperCamelCase(       nn.Module		):

		def __init__(					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					super().__init__()
					__a						:  Any									=     nn.Linear(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       bias=SCREAMING_SNAKE_CASE__    )
					__a						:  List[str]									=     nn.Linear(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       bias=SCREAMING_SNAKE_CASE__    )
					__a						:  List[Any]									=     nn.Linear(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       bias=SCREAMING_SNAKE_CASE__    )
					__a						:  Any									=     nn.Dropout(SCREAMING_SNAKE_CASE__    )
					__a						:  List[Any]									=     NewGELUActivation()



		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					__a						:  List[Any]									=     self.act(self.wi_a(SCREAMING_SNAKE_CASE__    )    )
					__a						:  List[str]									=     self.wi_a(SCREAMING_SNAKE_CASE__    )
					__a						:  int									=     hidden_gelu * hidden_linear
					__a						:  str									=     self.dropout(SCREAMING_SNAKE_CASE__    )

					__a						:  List[str]									=     self.wo(SCREAMING_SNAKE_CASE__    )
					return hidden_states






class 			_UpperCamelCase(       nn.Module		):

		def __init__(					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Any    ,       SCREAMING_SNAKE_CASE__		:    int=1e-6    ):



					'''simple docstring'''



					super().__init__()
					__a						:  int									=     nn.Parameter(torch.ones(SCREAMING_SNAKE_CASE__    )    )
					__a						:  Any									=     eps



		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  str									=     hidden_states.to(torch.floataa    ).pow(2    ).mean(-1    ,       keepdim=SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[Any]									=     hidden_states * torch.rsqrt(variance + self.variance_epsilon    )

					# convert into half-precision if necessary
					if self.weight.dtype in [torch.floataa, torch.bfloataa]:
								__a						:  Union[str, Any]									=     hidden_states.to(self.weight.dtype    )

					return self.weight * hidden_states






class 			_UpperCamelCase(       nn.Module		):

		def    __lowerCAmelCase  (					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    torch.Tensor    ):



					'''simple docstring'''



					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.044_715 * torch.pow(SCREAMING_SNAKE_CASE__    ,       3.0    ))    ))






class 			_UpperCamelCase(       nn.Module		):

		def __init__(					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    int    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



					super().__init__()
					__a						:  Any									=     nn.Linear(SCREAMING_SNAKE_CASE__    ,       out_features * 2    ,       bias=SCREAMING_SNAKE_CASE__    )



		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ):



					'''simple docstring'''



					__a						:  Dict									=     self.scale_bias(SCREAMING_SNAKE_CASE__    )
					__a						,					__a						:  List[str]									=     torch.chunk(SCREAMING_SNAKE_CASE__    ,       2    ,       -1    )
					__a						:  List[Any]									=     x * (1 + scale) + shift
					return x













"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)